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Research on Key Patent Identification for Solving the Technology Dilemma of “Neck Stuck” |
Zhu Jiahui1, Zhou Xiao1, Wang Bo2, Ren Qiaoyang1, Wang Dan3 |
1.School of Economics and Management, Xidian University, Xi’an 710126 2.Xi’an Institute of Electromechanical Information Technology, Xi’an 710065 3.School of Economics and Management, Guangxi Normal University, Guilin 541004 |
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Abstract With foreign restrictions on technology and products in the chip field, science and technology leading enterprises and institutions in China are working together to help break through the “Neck Stuck” technologies to achieve localization replacement. The key patents with breakthrough potential in “Neck Stuck” technology problems in China must be identified and targeted fine-grained solutions must be provided to help China achieve technology breakthrough and layout technology innovation strategy. On the basis of generating Chinese and foreign patent technology-effect texts with a large model, this study identifies the domestic key patents that can solve the technology problems of “Neck Stuck” by comparing the technology-effect texts of foreign core and domestic patents. The research innovation is mainly reflected in two aspects: (1) in the generation of technology-effect texts, the use of a large model as a text generation tool makes up for the mechanical extraction method and effectively reduces the degree of expert participation; (2) when identifying key patents, the deep correspondence of “problem-solution” is considered, and the Chinese and foreign patents are benchmarked from the underlying logic of technology rather than the surface of technical text, such that the identified Chinese key patents are more problem-oriented. Finally, this study considers the field of AI chips as an example to conduct an empirical research that verifies the feasibility of the proposed method and provides an effective way for China to break through the “Neck Stuck” technology dilemma.
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Received: 31 May 2024
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1 “小院高墙”是什么意思? 这得从美国对华政策说起……[EB/OL]. (2023-07-25). https://new.qq.com/rain/a/20230725A03LCA00. 2 院士热议国产芯片自主创新: 自主、开放“两条路”不可偏废[EB/OL]. (2018-06-05). https://www.thepaper.cn/newsDetail_forward_2173864. 3 陈旭, 江瑶, 熊焰, 等. 关键核心技术“卡脖子”问题的识别及应用: 以AI芯片为例[J]. 中国科技论坛, 2023(9): 17-27. 4 曹琨, 吴新年, 白光祖, 等. 基于专利文献的“卡脖子”技术识别研究——以数控机床领域为例[J]. 图书情报工作, 2023, 67(19): 80-91. 5 赵雪峰, 吴德林, 吴伟伟, 等. 基于深度学习与多分类轮询机制的高质量“卡脖子”技术专利识别模型——以专利申请文件为研究主体[J]. 数据分析与知识发现, 2023, 7(8): 30-45. 6 汤志伟, 李昱璇, 张龙鹏. 中美贸易摩擦背景下“卡脖子”技术识别方法与突破路径——以电子信息产业为例[J]. 科技进步与对策, 2021, 38(1): 1-9. 7 彭新敏, 张祺瑞, 刘电光. 后发企业超越追赶的动态过程机制——基于最优区分理论视角的纵向案例研究[J]. 管理世界, 2022, 38(3): 145-162. 8 郭彦彦, 吴福象. 基于TRIZ的中国关键技术突破路径研究——一个系统框架[J]. 科技进步与对策, 2024, 41(21): 1-10. 9 Wang J F, Zhang Z X, Feng L J, et al. Development of technology opportunity analysis based on technology landscape by extending technology elements with BERT and TRIZ[J]. Technological Forecasting and Social Change, 2023, 191: 122481. 10 Zhang D T, Wu X, Liu P, et al. Identification of product innovation path incorporating the FOS and BERTopic model from the perspective of invalid patents[J]. Applied Sciences, 2023, 13(13): 7987. 11 Sheu D D, Yen M Z. Systematic analysis and usage of harmful resources[J]. Computers & Industrial Engineering, 2020, 145: 106459. 12 赵敏, 史晓凌, 段海波. TRIZ入门及实践[M]. 北京: 科学出版社, 2009: 32-35. 13 Jeong Y, Yoon B. Development of patent roadmap based on technology roadmap by analyzing patterns of patent development[J]. Technovation, 2015, 39: 37-52. 14 Verbitsky M. Semantic TRIZ[J/OL]. The TRIZ Journal, (2004-02-05). https://the-trizjournal.com/semantic-triz/. 15 陈颖, 张晓林. 基于特征度和词汇模型的专利技术功效矩阵结构生成研究[J]. 现代图书情报技术, 2012(2): 53-59. 16 余梦霞, 张宇娥, 凌世婷, 等. 专利技术功效视域下的领域重点研发方向布局研究——以第三代功率半导体领域为例[J]. 图书情报工作, 2022, 66(17): 116-128. 17 郑彦宁, 化柏林. 句子级知识抽取在情报学中的应用分析[J]. 情报理论与实践, 2011, 34(12): 1-4. 18 Han X T, Zhu D H, Wang X F, et al. Technology opportunity analysis: combining SAO networks and link prediction[J]. IEEE Transactions on Engineering Management, 2021, 68(5): 1288-1298. 19 向姝璇, 李睿. 专利技术功效特征自动抽取方法探索——以6G领域为例[J]. 中国发明与专利, 2021, 18(4): 3-9. 20 Zhao W X, Zhou K, Li J, et al. A survey of large language models[OL]. (2025-03-11). https://arxiv.org/pdf/2303.18223. 21 Kaplan J, McCandlish S, Henighan T, et al. Scaling laws for neural language models[OL]. (2020-01-23). https://arxiv.org/pdf/2001.08361. 22 Wei J, Tay Y, Bommasani R, et al. Emergent abilities of large language models[OL]. (2022-10-26). https://arxiv.org/pdf/2206.07682v2. 23 赵浜, 曹树金. 国内外生成式AI大模型执行情报领域典型任务的测试分析[J]. 情报资料工作, 2023, 44(5): 6-17. 24 Jiang Y, Meng R, Huang Y, et al. Generating keyphrases for readers: a controllable keyphrase generation framework[J]. Journal of the Association for Information Science and Technology, 2023, 74(7): 759-774. 25 刘江峰, 刘雏菲, 齐月, 等. AIGC助力数字人文研究的实践探索: SikuGPT驱动的古诗词生成研究[J]. 情报理论与实践, 2023, 46(5): 23-31. 26 白如江, 陈启明, 张玉洁, 等. 基于ChatGPT+Prompt的专利技术功效实体自动生成研究[J]. 数据分析与知识发现, 2024, 8(4): 14-25. 27 Tseng Y H, Lin C J, Lin Y I. Text mining techniques for patent analysis[J]. Information Processing & Management, 2007, 43(5): 1216-1247. 28 Fattori M, Pedrazzi G, Turra R. Text mining applied to patent mapping: a practical business case[J]. World Patent Information, 2003, 25(4): 335-342. 29 胡菊香, 吕学强, 刘秀磊, 等. 专利技术功效短语获取研究[J]. 科学技术与工程, 2016, 16(14): 228-235. 30 Wei J, Bosma M, Zhao V Y, et al. Finetuned language models are zero-shot learners[OL]. (2022-02-08). https://arxiv.org/pdf/2109.01652. 31 Hu S D, Ding N, Wang H D, et al. Knowledgeable prompt-tuning: incorporating knowledge into prompt verbalizer for text classification[C]// Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2022: 2225-2240. 32 Gupta P, Jiao C, Yeh Y T, et al. InstructDial: improving zero and few-shot generalization in dialogue through instruction tuning[C]// Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2022: 505-525. 33 Hu E J, Shen Y L, Wallis P, et al. LoRA: Low-rank adaptation of large language models[OL]. (2021-10-16). https://arxiv.org/pdf/2106.09685. 34 Liyanage V, Buscaldi D, Nazarenko A. A benchmark corpus for the detection of automatically generated text in academic publications[C]// Proceedings of the Thirteenth Language Resources and Evaluation Conference. Stroudsburg: Association for Computational Linguistics, 2022: 4692-4700. 35 Zhang T Y, Kishore V, Wu F, et al. BERTScore: evaluating text generation with BERT[OL]. (2020-02-24). https://arxiv.org/pdf/1904.09675v3. 36 吕璐成, 韩涛, 陈芳, 等. 美国商业管制清单与专利自动映射方法及实证研究[J]. 情报学报, 2022, 41(1): 50-61. 37 朱宇婧, 陈芳, 赵萍, 等. 融合美国商业管制清单和专利的技术链关键路径识别研究——以EDA软件领域为例[J]. 图书情报工作, 2023, 67(21): 89-99. 38 刘康. 基于技术存在形式的技术垄断研究[J]. 科技进步与对策, 2012, 29(1): 15-20. 39 王彦超, 郭小敏, 余应敏. 反垄断与债务市场竞争中性[J]. 会计研究, 2020(7): 144-166. 40 Grimaldi M, Cricelli L. Indexes of patent value: a systematic literature review and classification[J]. Knowledge Management Research & Practice, 2020, 18(2): 214-233. 41 杨大飞, 杨武, 田雪姣, 等. 基于专利数据的核心技术识别模型构建及实证研究[J]. 情报杂志, 2021, 40(2): 47-54. 42 Feng L J, Niu Y X, Liu Z F, et al. Discovering technology opportunity by keyword-based patent analysis: a hybrid approach of morphology analysis and USIT[J]. Sustainability, 2020, 12(1): 136. 43 Wu H Q, Shen G Q, Lin X, et al. A transformer-based deep learning model for recognizing communication-oriented entities from patents of ICT in construction[J]. Automation in Construction, 2021, 125: 103608. 44 中国“新基建”万亿市场! 一文带您了解2025中国算力基础设施建设与发展深度分析[EB/OL]. (2025-02-20). https://www.doit.com.cn/p/528737.html. 45 2025年中国人工智能计算力发展评估报告[R]. 北京: IDC, 2025. 46 Du Z X, Qian Y J, Liu X, et al. GLM: general language model pretraining with autoregressive blank infilling[C]// Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2022: 320-335. 47 华芯通ARM服务器芯片昇龙4800正式量产 生态建设很给力[EB/OL]. (2018-11-27). https://www.sohu.com/a/278129739_465984. |
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